Concordance Between Electronic Health Record Data and Medicare Part D Claims Data for Oral Anticancer Drug Use.
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ABSTRACT:
SUBMITTER: Sharma M
PROVIDER: S-EPMC7193329 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
Concordance Between Electronic Health Record Data and Medicare Part D Claims Data for Oral Anticancer Drug Use.
Sharma Manvi M Johnson Michael L ML Zhao Hui H Giordano Sharon H SH Holmes Holly M HM
JAMA network open 20200401 4
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Project description:BackgroundMedicare is one of the world's largest health insurance programs. It provides health insurance to nearly 44 million beneficiaries whose entitlements are based on age, disability, or end-stage renal disease (ESRD). Data of these ESRD beneficiaries are collected in the US Renal Data System (USRDS), which includes comorbidity information entered at the time of dialysis initiation (medical evidence data), and are used to shape health care policy. One limitation of USRDS data is the lack of validation of these medical evidence comorbidities against other comorbidity data sources, such as medical claims data.MethodsWe examined the potential for discordance between USRDS Medical Evidence and medical claims data for 11 comorbid conditions amongst Medicare beneficiaries in 2011-2013 via sensitivity, specificity, kappa and hierarchical logistic regression.ResultsAmong 61,280 patients, most comorbid conditions recorded on the Medical Evidence forms showed high specificity (>0.9), compared to prior medical claims as reference standard. However, both sensitivity and kappa statistics varied greatly and tended to be low (most <0.5). Only diabetes appeared accurate, whereas tobacco use and drug dependence showed the poorest quality (sensitivity and kappa <0.1). Institutionalization and patient region of residency were associated with data discordance for six and five comorbidities out of 11, respectively, after conservative adjustment of multiple testing. Discordance appeared to be non-informative for congestive heart failure but was most varied for drug dependence.ConclusionsWe conclude that there is no improvement in comorbidity data quality in incident ESRD patients over the last two decades. Since these data are used in case-mix adjustment for outcome and quality of care metrics, the findings in this study should press regulators to implement measures to improve the accuracy of comorbidity data collection.
| S-EPMC6065459 | biostudies-literature
Project description:ContextPalliative care can improve the lives of people with serious illness, yet clear operational definitions of this population do not exist. Prior efforts to identify this population have not focused on Medicare Advantage (MA) and commercial health plan enrollees.ObjectivesWe aimed to operationalize our conceptual definition of serious illness to identify those with serious medical conditions (SMC) among commercial insurance and MA enrollees, and to compare the populations identified through electronic health record (EHR) or claims data sources.MethodsWe used de-identified claims and EHR data from the OptumLabs Data Warehouse (2016-2017), to identify adults age ≥18 with SMC and examine their utilization and mortality. Within the subset found in both data sources, we compared the performance of claims and EHR data.ResultsWithin claims, SMC was identified among 10% of those aged ≥18 (5.4% ages 18-64, 27% age ≥65). Within EHR, SMC was identified among 9% of those aged ≥18 (5.6% ages 18-64, 21% ages ≥65). Hospital, emergency department and mortality rates were similar between the EHR and claims-based groups. Only 50% of people identified as having SMC were recognized by both data sources.ConclusionThese results demonstrate the feasibility of identifying adults with SMC in a commercially insured population, including MA enrollees; yet separate use of EHR or claims result in populations that differ. Future research should examine methods to combine these data sources to optimize identification and support population management, quality measurement, and research to improve the care of those living with serious illness.
| S-EPMC8419009 | biostudies-literature
Project description:PURPOSE:Data from claims and electronic medical records (EMRs) are frequently used to identify clinical events (eg, cancer diagnosis, stroke). However, accurately determining the time of clinical events can be challenging, and the methods used to generate time estimates are underdeveloped. We sought to develop an approach to determine the time of a clinical event-cancer recurrence-using high-dimensional longitudinal structured data. METHODS:Manual chart abstraction provided information regarding the actual time of cancer recurrence. These data were linked to claims from Medicare or structured EMR data from the Cancer Research Network, which were used to determine time of recurrence for patients with lung or colorectal cancer. We analyzed the longitudinal profile of codes that could help determine the time of recurrence, adjusted for systematic differences between code dates and recurrence dates, and integrated time estimates from different codes to empirically derive an optimal algorithm. RESULTS:We identified twelve code groups that could help determine the time of recurrence. Using claims data for patients with lung cancer, the optimal algorithm consisted of three code groups and provided an average prediction error of 4.8 months. Using EMR data or applying this approach to patients with colorectal cancer yielded similar results. CONCLUSION:Time estimates were improved by selecting codes not necessarily the same as those used to identify recurrence, combining time estimates from multiple code groups, and adjusting for systematic bias between code dates and recurrence dates. Improving the accuracy of time estimates for clinical events can facilitate research, quality measurement, and process improvement.
| S-EPMC6338474 | biostudies-literature
Project description:Because drug-induced interstitial pneumonia (DIP) is a serious adverse drug reaction, its quantitative risk with individual medications should be taken into due consideration when selecting a medicine. We developed an algorithm to detect DIP using medical record data accumulated in a hospital. Chest computed tomography (CT) is mainly used for the diagnosis of IP, and chest X-ray reports, KL-6, and SP-D values are used to support the diagnosis. The presence of IP in the reports was assessed by a method using natural language-processing, in which IP was estimated according to the product of the likelihood ratio of characteristic keywords in each report. The sensitivity and the specificity of the method for chest CT reports were 0.92 and 0.97, while those for chest X-ray reports were 0.83 and 1, respectively. The occurrence of DIP was estimated by the patterns of presence of IP before, during, and after the administration of the target medicine. The occurrence rate of DIP in cases administered Gefitinib; Methotrexate (MTX); Tegafur, Gimeracil, and Oteracil potassium (TS-1); and Tegafur and Uracil (UTF) was 6.0%, 2.3%, 1.4%, and 0.7%, respectively. The estimated DIP cases were checked by having the medical records independently reviewed by medical doctors. By chart review, the positive predictive values of DIP against Gefitinib, MTX, TS-1, and UFT were 69.2%, 44.4%, 58.6%, and 77.8%, respectively. Although the cases extracted by this method included some that did not have DIP, this method can estimate the relative risk of DIP between medicines.
| S-EPMC6043691 | biostudies-literature
Project description:BackgroundElectronic health record (EHR) prediction models may be easier to use in busy clinical settings since EHR data can be auto-populated into models. This study assessed whether adding functional status and/or Medicare claims data (which are often not available in EHRs) improves the accuracy of a previously developed Veterans Affairs (VA) EHR-based mortality index.MethodsThis was a retrospective cohort study of veterans aged 75 years and older enrolled in VA primary care clinics followed from January 2014 to April 2020 (n = 62,014). We randomly split participants into development (n = 49,612) and validation (n = 12,402) cohorts. The primary outcome was all-cause mortality. We performed logistic regression with backward stepwise selection to develop a 100-predictor base model using 854 EHR candidate variables, including demographics, laboratory values, medications, healthcare utilization, diagnosis codes, and vitals. We incorporated functional measures in a base + function model by adding activities of daily living (range 0-5) and instrumental activities of daily living (range 0-7) scores. Medicare data, including healthcare utilization (e.g., emergency department visits, hospitalizations) and diagnosis codes, were incorporated in a base + Medicare model. A base + function + Medicare model included all data elements. We assessed model performance with the c-statistic, reclassification metrics, fraction of new information provided, and calibration plots.ResultsIn the overall cohort, mean age was 82.6 years and 98.6% were male. At the end of follow-up, 30,263 participants (48.8%) had died. The base model c-statistic was 0.809 (95% CI 0.805-0.812) in the development cohort and 0.804 (95% CI 0.796-0.812) in the validation cohort. Validation cohort c-statistics for the base + function, base + Medicare, and base + function + Medicare models were 0.809 (95% CI 0.801-0.816), 0.811 (95% CI 0.803-0.818), and 0.814 (95% CI 0.807-0.822), respectively. Adding functional status and Medicare data resulted in similarly small improvements among other model performance measures. All models showed excellent calibration.ConclusionsIncorporation of functional status and Medicare data into a VA EHR-based mortality index led to small but likely clinically insignificant improvements in model performance.
| S-EPMC9118715 | biostudies-literature
Project description:BackgroundAsthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome.ObjectiveWe propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population.MethodsWe performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset.ResultsThere were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91-0.94) and 0.94 (0.93-0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms.ConclusionWe were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.
| S-EPMC6051766 | biostudies-literature
Project description:BackgroundIn patients with atrial fibrillation (AF) who survive an anticoagulant-related intracranial hemorrhage (ICH), the benefits of restarting oral anticoagulation (OAC) remain unclear.ObjectiveIn this study, we sought to determine the effectiveness and safety associated with resumption of OAC in atrial fibrillation patients who survive an ICH.MethodsUsing 2010-2016 Medicare claims data, we identified patients with non-valvular AF who experienced an OAC-related ICH and survived at least 6 weeks after the ICH (n = 1502). The primary outcomes included the composite of ischemic stroke and transient ischemic attack (TIA), thromboembolism (TE), a composite of ischemic stroke/TIA and TE, recurrent ICH, and all-cause mortality. We constructed Cox proportional hazard models to evaluate the association between post-ICH OAC resumption, which was measured in a time-dependent manner, and the risk of primary outcomes, while controlling for a comprehensive list of covariates.ResultsAmong patients who survived an ICH, 69% reinitiated OAC within 6 weeks of the event, and among those who resumed OAC, 83% restarted warfarin. There was no significant difference in the risk of ischemic stroke/TIA (hazard ratio [HR] 0.87, 95% confidence interval [CI] 0.62-1.21), TE (HR 0.85, 95% CI 0.55-1.32), and ischemic stroke/TIA/TE (HR 0.81, 95% CI 0.61-1.07) between post-ICH OAC use and non-use. Post-ICH OAC use was associated with a lower risk of recurrent ICH (HR 0.62, 95% CI 0.41-0.95) and all-cause mortality (HR 0.48, 95% CI 0.37-0.62) compared with non-OAC use.ConclusionsIn AF patients who survived an ICH, restarting OAC was not associated with a greater risk of recurrent ICH. Evidence from randomized controlled studies is needed to further clarify the clinical benefit of restarting OAC in this high-risk population. Further evaluation of which individuals benefit from restarting OAC is also needed to provide more clinical guidance.
| S-EPMC7274872 | biostudies-literature
Project description:ObjectivesMuch has been written about trends in Medicare Part D formulary design and consumers' choice of plans, but little is known about the magnitude of claims rejections or their clinical and administrative implications. Our objective was to study the overall rate at which Part D claims are rejected, whether these rates differ across plans, drugs, and medication classes, and how these rejection rates and reasons have evolved over time.Study design and methodsWe performed descriptive analyses of data on paid and rejected Part D claims submitted by 1 large national longterm care pharmacy from 2006 to 2010. In each of the 5 study years, data included approximately 450,000 Medicare beneficiaries living in long-term care settings with approximately 4 million Part D drug claims. Claims rejection rates and reasons for rejection are tabulated for each study year at the plan, drug, and class levels.ResultsNearly 1 in 6 drug claims was rejected during the first 5 years of the Medicare Part D program, and this rate has increased over time. Rejection rates and reasons for rejection varied substantially across drug products and Part D plans. Moreover, the reasons for denials evolved over our study period. Coverage has become less of a factor in claims rejections than it was initially and other formulary tools such as drug utilization review, quantity-related coverage limits, and prior authorization are increasingly used to deny claims.ConclusionsExamining claims rejection rates can provide important supplemental information to assess plans' generosity of coverage and to identify potential areas of concern.
| S-EPMC3540110 | biostudies-literature
Project description:BackgroundThe population-based Surveillance, Epidemiology, and End Results (SEER) registries collect information on first-course treatment, including surgery, chemotherapy, radiation therapy, and hormone therapy. However, the SEER program does not release data on chemotherapy or hormone therapy due to uncertainties regarding data completeness. Activities are ongoing to investigate the opportunity to supplement SEER treatment data with other data sources.MethodsUsing the linked SEER-Medicare data, we examined the validity of the SEER data to identify receipt of chemotherapy and radiation therapy among those aged 65 and older diagnosed from 2000 to 2006 with bladder, female breast, colorectal, lung, ovarian, pancreas, or prostate cancer and hormone therapy among men diagnosed with prostate cancer at age 65 or older. Treatment collected by SEER was compared with treatment as determined by Medicare claims, using Medicare claims as the gold standard. The κ, sensitivity, specificity, positive predictive values, and negative predictive values were calculated for the receipt of each treatment modality.ResultsThe overall sensitivity of SEER data to identify chemotherapy, radiation, and hormone therapy receipt was moderate (68%, 80%, and 69%, respectively) and varied by cancer site, stage, and patient characteristics. The overall positive predictive value was high (>85%) for all treatment types and cancer sites except chemotherapy for prostate cancer.ConclusionsSEER data should not generally be used for comparisons of treated and untreated individuals or to estimate the proportion of treated individuals in the population. Augmenting SEER data with other data sources will provide the most accurate treatment information.
| S-EPMC4981219 | biostudies-literature
Project description:BackgroundBaseline and trend data for oral and pharyngeal cancer incidence is limited. A new algorithm was derived using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database to create an algorithm to identify incident cases of oral and pharyngeal cancer using Medicare claims.MethodsUsing a split-sample approach, Medicare claims' procedure and diagnosis codes were used to generate a new algorithm to identify oral and pharyngeal cancer cases and validate its operating characteristics.ResultsThe algorithm had high sensitivity (95%) and specificity (97%), which varied little by age group, sex, and race and ethnicity.ConclusionExamples of the utility of this algorithm and its operating characteristics include using it to derive baseline and trend estimates of oral and pharyngeal cancer incidence. Such measures could be used to provide incidence estimates where they are lacking or to serve as comparator estimates for tumor registries.
| S-EPMC3538504 | biostudies-literature